max_pool1d(self, kernel_size, stride, padding, dilation, ceil_mode) Output Segmentation fault (core dumped) Versions PyTorch version: 2.5.0a0+git32f585d Is debug build: False CUDA used to build PyTorch: None ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.4 LTS (x86_64) GCC ...
max_pool1d(i, i.size(2)).squeeze(2) for i in x] # [(N, Co), ...]*len(Ks) x = torch.cat(x, 1) ''' x1 = self.conv_and_pool(x,self.conv13) #(N,Co) x2 = self.conv_and_pool(x,self.conv14) #(N,Co) x3 = self.conv_and_pool(x,self.conv15) #(N,Co) x =...
TORCH_CHECK(false, "MaxPool", kSpatialDim, "D is not supported."); } };TORCH_LIBRARY_IMPL(quantized, QuantizedCPU, m) { m.impl("max_pool2d", TORCH_FN(QMaxPool2D_arr_args::run)); m.impl("max_pool1d", TORCH_FN(QMaxPool_arr_args<1>::run)); ...
torch.nn.functional.max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False) torch.nn.functional.max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False) torch.nn.functional.max_pool3d(input...
1)torch.nn.MaxPool1d它用于在由多个输入平面组成的输入信号上应用一维最大池。 2)torch.nn.MaxPool2d它用于在由多个输入平面组成的输入信号上应用2D max池。 3)torch.nn.MaxPool3d它用于在由多个输入平面组成的输入信号上应用3D max池。 4)torch.nn.MaxUnpool1d它用于计算MaxPool1d的局部逆。
max_pool1d, 2: F.max_pool2d, 3: F.max_pool3d}[spatial_dims](input=pad, kernel_size=size[2:], stride=stride[2:], padding=0, dilation=dilation[2:], return_indices=with_index) return result Example #4Source File: deep_lift.py From captum with BSD 3-Clause "New" or "Revised" ...
nn.MaxPool1d: 一维最大池化。 nn.MaxPool2d:二维最大池化。一种下采样方式。没有需要训练的参数。 nn.MaxPool3d:三维最大池化。 nn.MaxUnpool1d:一维最大逆池化。 nn.MaxUnpool2d:二维最大逆池化,将包含最大值索引的输出作为输入,其中所有非最大值都设置为零。
torch.nn.functionaltorch.nn.functional.adaptive_avg_pool1d()torch.nn.functional.adaptive_avg_pool2d()torch.nn.functional.adaptive_avg_pool3d()torch.nn.functional.adaptive_max_pool1d()torch.nn.functional.adaptive_max_pool2d()torch.nn.functional.adaptive_max_pool3d()torch.nn.functional.affine_grid...
torch.nn.functional.max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False) torch.nn.functional.max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False) ...
torch.nn.functional.max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)source对由几个输入平面组成的输入进行1D最大池化。 有关详细信息和输出形状,参考MaxPool1dtorch.nn.functional.max_pool2d(input, kernel_size, stride=None, padding=0, ...